Biology subjects

Sanders, L.

Publications and source records attributed to Sanders, L..

6 recordsLinked to original sources

bioRxiv2026

First genetic detection and ongoing eDNA monitoring of the golden mussel (Limnoperna fortunei) in California

Here, we report the first genetic confirmation of golden mussels (Limnoperna fortunei) in North America, and the subsequent development, optimization, and deployment of golden mussel eDNA monitoring procedures. Aquatic species invasions are economically costly, disrupt ecosystem functionality, and impact native aquatic communities. Early detection of new invasive species enables rapid response via implementation of effective eradication or control measures and is key for reducing harmful outcomes. Initial species detection and taxonomic identification can be aided by genetic methods that have high detection sensitivity and accuracy. Genetic methods such as environmental DNA (eDNA) sampling can be used to detect invasive species before they become established in new systems, providing an early alert system to inform resource managers. Golden mussels were first detected in North America in October 2024 near the Port of Stockton in the San Francisco Estuary (SFE). The SFE is particularly vulnerable to invasion due to the access and connectivity provided by the presence of engineering infrastructure and shipping lanes. Collaborative efforts between public agencies and academic institutions are underway to develop a coordinated detection and response plan. Early detection followed by a rapid response is the best defense against prolific invasive species, such as the golden mussel.

genetics↗

bioRxiv2026

A systematic imputation framework for sparse, multimodal space biology datasets: application to retinal imaging and omics from the RR9 mission

Missing data is a fundamental challenge in space biology, where high experimental costs, limited sample availability, and tissue allocation constraints produce datasets that are sparse, multimodal, and heterogeneous. We present a systematic four-stage framework for diagnosing, implementing, and validating data imputation strategies tailored to these characteristics, and demonstrate its application to retinal imaging and omics data from the NASA Rodent Research 9 (RR9) mission. Using logistic regression-based missingness diagnosis, we identify a Missing At Random (MAR) mechanism driven by experimental design constraints across nine assay modalities. We implement and optimize three imputation strategies: K-Nearest Neighbors (KNN), Multiple Imputation by Chained Equations with weak ElasticNet regularization (MICE-Elastic), and a per-column hybrid strategy, evaluated against a random sample imputer baseline. Validation across seven complementary metrics including supervised classification, unsupervised clustering, correlation structure preservation, masked value recovery, cross-dataset generalization, and permutation testing reveals that MICE-Elastic and the Hybrid strategy preserve genuine biological signal in both RNA-seq and TUNEL modalities, while KNN and the random sample imputer do not despite achieving comparable cross-validation accuracy. A critical finding is that imputation substantially improves supervised classification performance while consistently degrading unsupervised clustering structure, a trade-off researchers must understand before applying these methods. This framework provides practical, actionable guidance for space biologists and data scientists managing sparse multimodal datasets, and represents a foundational step toward digital twin development for space medicine.

bioinformatics↗

bioRxiv2023

Targeting Transferrin Receptor to Transport Antisense Oligonucleotides Across the Blood-Brain Barrier

Antisense oligonucleotides (ASO) are promising therapies for neurological disorders, though they are unable to cross the blood-brain barrier (BBB) and must be delivered directly to the central nervous system (CNS). Here, we use a human transferrin receptor (TfR)-binding molecule to transport ASO across the BBB in mice and non-human primates, termed oligonucleotide transport vehicle (OTV). Systemically delivered OTV drives significant, cumulative, and sustained knockdown of the ASO target across multiple CNS regions and all major cell types. Further, systemic OTV delivery enables more uniform ASO biodistribution and knockdown compared to two other clinically relevant ASO delivery routes: a standard, high affinity TfR antibody, or direct ASO delivery to the CSF. Together, our data support systemically delivered OTV as a potential therapeutic platform for neurological disorders. One-Sentence SummarySystemically dosed OTV delivered via TfR1 targeting shows widespread and cumulative target knockdown in the mouse and NHP CNS.

neuroscience↗

bioRxiv2019

Framework for determining accuracy of RNA sequencing data for gene expression profiling of single samples

BackgroundThe accuracy of gene expression as measured by RNA sequencing (RNA-Seq) is dependent on the amount of sequencing performed. However, some types of reads are not informative for determining this accuracy. Unmapped and non-exonic reads do not contribute to gene expression quantification. Duplicate reads can be the product of high gene expression or technical errors. FindingsWe surveyed bulk RNA-Seq datasets from 2179 tumors in 48 cohorts to determine the fractions of uninformative reads. Total sequence depth was 0.2-668 million reads (median (med.) 61 million; interquartile range (IQR) 53 million). Unmapped reads constitute 1-77% of all reads (med. 3%; IQR 3%); duplicate reads constitute 3-100% of mapped reads (med. 27%; IQR 30%); and non-exonic reads constitute 4-97% of mapped, non-duplicate reads (med. 25%; IQR 21%). Informative reads--Mapped, Exonic, Non-duplicate (MEND) reads--constitute 0-79% of total reads (med. 50%; IQR 31%). Further, we find that MEND read counts have a 0.22 Pearson correlation to the number of genes expressed above 1 Transcript Per Million, while total reads have a correlation of -0.05. ConclusionsSince the fraction of uninformative reads vary, we propose using only definitively informative reads, MEND reads, for the purposes of asserting the accuracy of gene expression measured in a bulk RNA-Seq experiment. We provide a Docker image containing 1) the existing required tools (RSeQC, sambamba and samblaster) and 2) a custom script. We recommend that all results, sensitivity studies and depth recommendations use MEND units.

genomics↗

bioRxiv2019

CADHERIN-11 BLOCKADE REDUCES INFLAMMATION DRIVEN FIBROTIC REMODELING AND IMPROVES OUTCOMES AFTER MYOCARDIAL INFARCTION

BackgroundOver one million Americans experience myocardial infarction (MI) every year, and the resulting scar and subsequent cardiac fibrosis contribute to heart failure and death. A specialized cell-cell adhesion protein, cadherin-11 (CDH11), contributes to inflammation and fibrosis in rheumatoid arthritis, pulmonary fibrosis, and aortic valve calcification but has not yet been studied in the context of cardiac remodeling after MI. We hypothesized that targeting CDH11 function after MI would reduce inflammation-driven fibrotic remodeling and infarct expansion to improve functional outcomes in mice. MethodsMI was induced by ligation of the left anterior descending artery in transgenic mice with reduced or ablated CDH11, wild type mice receiving bone marrow transplants from Cdh11 transgenic animals, and wild type mice treated with a functional blocking antibody against CDH11 (SYN0012). Cardiac function was measured by echocardiography, expression of cell populations was quantified by flow cytometry, and tissue remodeling by altered histological assessment and transcription of inflammatory and pro-angiogenic genes by qPCR. Co-culture was used to assess interactions between cardiac fibroblasts and macrophages. ResultsMI increased transcription of Cdh11 in non-cardiomyocyte cells. Mice with deletion of Cdh11 and wild type mice receiving bone marrow transplants from Cdh11 transgenic animals had improved cardiac function and dimensions after MI. Animals given SYN0012 had improved cardiac function, reduced tissue remodeling, and altered transcription of inflammatory and proangiogenic genes. Targeting CDH11 also reduced the number of bone marrow-derived myeloid cells and increased pro-angiogenic cells in the heart three days after MI, consistent with a decrease in transcription and expression of IL-6 in the infarct region. Cardiac fibroblast and macrophage interactions led to an increase in IL-6 secretion that was reduced with SYN0012 treatment in vitro. ConclusionsOur findings suggest that CDH11-expressing cells contribute to inflammation-driven fibrotic remodeling after MI, and that targeting CDH11 with a blocking antibody improves cardiac function after MI. This improvement is likely mediated by altered recruitment of bone marrow-derived cells, thereby limiting the macrophage-induced expression of IL-6 by fibroblasts and promoting vascularization.

molecular biology↗

bioRxiv2017

Optimal Vaccination of a General Population Network via Genetic Algorithms

Herein we extend the work from Patel et al. (1) to find the approximate, optimal distribution of vaccinations of a virus spreading on a network with the use of Genetic Algorithms (GAs). We situate our investigation in an online social network, a Facebook graph of ~4000 nodes. Within this framework we investigate the performance of an optimized vaccine distribution scheme against that of a well known heuristic scheme: the vaccination of highly ranked nodes. We include also a baseline scheme of vaccinating random nodes. We show the algorithm is superior to rank scheme in low vaccine coverages, and performs comparably for most other coverage values, lending support to the optimality of this heuristic measure.

epidemiology↗